Method and apparatus for outputting information

ABSTRACT

Embodiments of the present disclosure relate to a method and apparatus for outputting information. The method may include: acquiring point cloud data and image data collected by a vehicle during a driving process; determining a plurality of time thresholds based on a preset time threshold value range; executing following processing for each time threshold: identifying obstacles included in each point cloud frame and each image frame respectively; determining a similarity between the obstacles; determining, in response to the similarity being greater than a preset similarity threshold, whether a time interval between the point cloud frame and the image frame corresponding to two similar obstacles is less than the time threshold; and processing recognized obstacles based on a determining result, to determine the number of obstacles; and determining, based on a plurality of numbers, and outputting a target time threshold.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to Chinese Application No.201910906113.0, filed on Sep. 24, 2019 and entitled “Method andApparatus for Outputting Information,” the entire disclosure of which ishereby incorporated by reference.

TECHNICAL FIELD

Embodiments of the present disclosure relate to the field of computertechnology, and specifically to a method and apparatus for outputtinginformation.

BACKGROUND

At present, lidar ranging has been widely used in the fields, such asautonomous driving and auxiliary driving, due to its excellentcharacteristics and strong adaptability to the external environment. Inthe application scenarios of data collected by a lidar, many parametersoften need to be adjusted. Manual adjustment of these parameters isoften time-consuming and labor-consuming.

SUMMARY

Embodiments of the present disclosure propose a method and apparatus foroutputting information.

In a first aspect, an embodiment of the present disclosure provides amethod for outputting information, including: acquiring obstacle datacollected by a vehicle during a driving process, the obstacle dataincluding point cloud data and image data; determining a plurality oftime thresholds based on a preset time threshold value range; executingfollowing processing for each time threshold: identifying obstaclesincluded in each point cloud frame in the point cloud data and eachimage frame in the image data respectively; determining a similaritybetween an obstacle in each point cloud frame and an obstacle in eachimage frame; determining, in response to the similarity being greaterthan a preset similarity threshold, whether a time interval between thepoint cloud frame and the image frame corresponding to two similarobstacles is less than the time threshold; and processing recognizedobstacles based on a determining result, to determine a number ofobstacles; and determining, based on obtained a plurality of numbers,and outputting a target time threshold.

In some embodiments, the determining a plurality of time thresholdsbased on a preset time threshold value range includes: selecting aplurality of points in the time threshold value range at a preset timeinterval, as the plurality of time thresholds.

In some embodiments, the processing recognized obstacles based on adetermining result, to determine a number of obstacles includes:associating, in response to the time interval being less than the timethreshold, the two similar obstacles as the same obstacle; andnon-associating, in response to the time interval being greater than orequal to the time threshold, the two similar obstacles as the sameobstacle.

In some embodiments, the method further includes: fusing, in response tothe associating the two similar obstacles as the same obstacle, the twosimilar obstacles based on the point cloud frame and the image framecorresponding to the two similar obstacles.

In some embodiments, the determining, based on obtained a plurality ofnumbers, and outputting a target time threshold includes: determining anumber-time threshold curve based on the plurality of numbers and a timethreshold corresponding to each number; and determining a slope of thecurve at each time threshold, and determining the target time thresholdbased on each slope.

In some embodiments, the determining the target time threshold based oneach slope includes: determining a maximum value of absolute values ofthe slopes; and using a time threshold corresponding to the maximumvalue as the target time threshold.

In a second aspect, an embodiment of the present disclosure provides anapparatus for outputting information, including: a data acquiring unitconfigured to acquire obstacle data collected by a vehicle during adriving process, the obstacle data including point cloud data and imagedata; a threshold determining unit configured to determine a pluralityof time thresholds based on a preset time threshold value range; a dataprocessing unit configured to execute following processing for each timethreshold: identifying obstacles included in each point cloud frame inthe point cloud data and each image frame in the image datarespectively; determining a similarity between an obstacle in each pointcloud frame and an obstacle in each image frame; determining, inresponse to the similarity being greater than a preset similaritythreshold, whether a time interval between the point cloud frame and theimage frame of two similar obstacles is less than the time threshold;and processing recognized obstacles based on a determining result, todetermine a number of obstacles; and a target determining unitconfigured to determine, based on obtained a plurality of numbers, andoutput a target time threshold.

In some embodiments, the threshold determining unit is furtherconfigured to: select a plurality of points in the time threshold valuerange at a preset time interval, as the plurality of time thresholds.

In some embodiments, the data processing unit is further configured to:associate, in response to the time interval being less than the timethreshold, the two similar obstacles as the same obstacle; andnon-associate, in response to the time interval being greater than orequal to the time threshold, the two similar obstacles as the sameobstacle.

In some embodiments, the apparatus further includes: a data fusing unitconfigured to fuse, in response to the associating the two similarobstacles as the same obstacle, the two similar obstacles based on thepoint cloud frame and the image frame corresponding to the two similarobstacles.

In some embodiments, the target determining unit is further configuredto: determine a number-time threshold curve based on the plurality ofnumbers and a time threshold corresponding to each number; and determinea slope of the curve at each time threshold, and determine the targettime threshold based on each slope.

In some embodiments, the target determining unit is further configuredto: determine a maximum value of absolute values of the slopes; and usea time threshold corresponding to the maximum value as the target timethreshold.

In a third aspect, an embodiment of the present disclosure provides anelectronic device, the electronic device including: one or moreprocessors; and a storage apparatus, storing one or more programs, wherethe one or more programs, when executed by the one or more processors,cause the one or more processors to implement any embodiment of themethod according to the first aspect.

In a fourth aspect, an embodiment of the present disclosure provides acomputer readable medium, storing a computer program thereon, where thecomputer program, when executed by a processor, implements anyembodiment of the method according to the first aspect.

The method and apparatus for outputting information provided by someembodiments of the present disclosure may first acquire obstacle datacollected by a vehicle during a driving process, the obstacle data mayinclude point cloud data and image data; then may determine a pluralityof time thresholds based on a preset time threshold value range; mayexecute processing for each time threshold: identifying obstaclesincluded in each point cloud frame in the point cloud data and eachimage frame in the image data respectively; then determining asimilarity between an obstacle in each point cloud frame and an obstaclein each image frame; determining, in response to the similarity beinggreater than a preset similarity threshold, whether a time intervalbetween the point cloud frame and the image frame of two similarobstacles is less than the time threshold; and processing recognizedobstacles based on a determining result, to determine the number ofobstacles; and finally determine, based on obtained a plurality ofnumbers, and output a target time threshold. The method of the presentembodiment may determine the time threshold during the obstacle dataprocessing based on the number of obstacles, thereby achieving automaticadjustment of the time threshold without the need of manual adjustment.

BRIEF DESCRIPTION OF THE DRAWINGS

After reading detailed description of non-limiting embodiments withreference to the following accompanying drawings, other features,objectives and advantages of the present disclosure will become moreapparent.

FIG. 1 is a diagram of an example system architecture in whichembodiments of the present disclosure may be implemented;

FIG. 2 is a flowchart of a method for outputting information accordingto an embodiment of the present disclosure;

FIG. 3 is a schematic diagram of an application scenario of a method foroutputting information according to an embodiment of the presentdisclosure;

FIG. 4 is a flowchart of determining a target time threshold in themethod for outputting information according to an embodiment of thepresent disclosure;

FIG. 5 is a schematic structural diagram of an apparatus for outputtinginformation according to an embodiment of the present disclosure; and

FIG. 6 is a schematic structural diagram of a computer system adapted toimplement an electronic device of embodiments of the present disclosure.

DETAILED DESCRIPTION OF EMBODIMENTS

Embodiments of present disclosure will be described below in detail withreference to the accompanying drawings. It should be appreciated thatthe specific embodiments described herein are merely used for explainingthe relevant disclosure, rather than limiting the disclosure. Inaddition, it should be noted that, for the ease of description, only theparts related to the relevant disclosure are shown in the accompanyingdrawings.

It should also be noted that some embodiments in the present disclosureand some features in the disclosure may be combined with each other on anon-conflict basis. Features of the present disclosure will be describedbelow in detail with reference to the accompanying drawings and incombination with embodiments.

FIG. 1 shows an example system architecture 100 in which a method foroutputting information or an apparatus for outputting information ofembodiments of the present disclosure may be implemented.

As shown in FIG. 1, the system architecture 100 may include autonomousvehicles 101, 102, and 103, a network 104, and a server 105. The network104 serves as a medium providing a communication link between theautonomous vehicles 101, 102, and 103, and the server 105. The network104 may include various types of connections, such as wired or wirelesscommunication links, or optical cables.

The autonomous vehicles 101, 102, and 103 may be provided with varioussensors, such as a lidar or an image collecting apparatus, to collectpoint cloud data or image data of a driving environment of theautonomous vehicles 101, 102, and 103. The autonomous vehicles 101, 102,and 103 may be further provided with various electronic devices, such asa navigation apparatus, an autonomous vehicle controller, an anti-lockbraking system, and a braking force distribution system. The autonomousvehicles 101, 102, and 103 may be vehicles that include an autonomousdriving mode, including both fully autonomous vehicles and vehicles thatcan be switched to the autonomous driving mode.

The server 105 may be a server that provides various services, such as aback-end server for processing obstacle data collected by the vehicles101, 102, and 103. The back-end server can process, e.g., analyze,received data, such as the obstacle data, and return the processingresult (e.g., a target time threshold) to the vehicles 101, 102, and103.

It should be noted that the server 105 may be hardware, or may besoftware. When the server 105 is hardware, the server may be implementedas a distributed server cluster composed of a plurality of servers, orbe implemented as a single server. When the server 105 is software, theserver may be implemented as a plurality of software programs orsoftware modules (e.g., software programs or software modules forproviding distributed services), or may be implemented as a singlesoftware program or software module. This is not specifically limitedhere.

It should be noted that the method for outputting information providedby some embodiments of the present disclosure may be executed by thevehicles 101, 102, and 103, or may be executed by the server 105.Accordingly, the apparatus for outputting information may be provided inthe vehicles 101, 102, and 103, or may be provided in the server 105.

It should be understood that the numbers of vehicles, networks, andservers in FIG. 1 are merely illustrative. Any number of vehicles,networks, and servers may be provided based on actual requirements.

With further reference to FIG. 2, a process 200 of a method foroutputting information according to an embodiment of the presentdisclosure is shown. The method for outputting information includesfollowing steps.

Step 201: acquiring obstacle data collected by a vehicle during adriving process.

In the present embodiment, an executing body (e.g., the server 105 shownin FIG. 1) of the method for outputting information may acquire theobstacle data collected by the vehicle during the driving processthrough a wired connection or connection. The obstacle data may includepoint cloud data and image data. The point cloud data may include aplurality of point cloud frames, and each point cloud frame may includea plurality of point cloud points. The image data may include aplurality of image frames. The vehicle may be provided with a lidarsensor and a camera, to collect point cloud data and image data of asurrounding environment of the vehicle during driving.

Step 202: determining a plurality of time thresholds based on a presettime threshold value range.

In the present embodiment, the executing body may acquire a preset timethreshold value range, and the time threshold value range may bedetermined based on values of a plurality of preset time thresholds. Forexample, the plurality of preset time thresholds may include 0.5 ms, 1.0ms, and 1.5 ms. Each time threshold may be set by a skilled person basedon his own experience. The executing body may obtain the plurality oftime thresholds by taking values at intervals of a preset duration inthe time threshold value range. For example, the time threshold valuerange is 0.5 ms-1.5 ms. The executing body may take a value every 0.1ms, and then may obtain the plurality of time thresholds, which are 0.5ms, 0.6 ms, 0.7 ms, . . . , 1.5 ms, respectively. Alternatively, theexecuting body may also randomly select the plurality of time thresholdsfrom the above time threshold value range.

In some alternative implementations of the present embodiment, theexecuting body may determine the plurality of time thresholds throughthe following steps that are not shown in FIG. 2: selecting a pluralityof points in the time threshold value range at a preset time interval,as the plurality of time thresholds.

In the present implementation, the executing body may select theplurality of points in the time threshold value range at the preset timeinterval, as the plurality of time thresholds. Specifically, the timethreshold value range may be divisible by the time interval.

Step 203: executing the following processing steps 2031-2034 for eachtime threshold.

After obtaining the plurality of time thresholds, the executing body mayperform the processing steps 2031-2034 for each time threshold.

Step 2031: identifying obstacles included in each point cloud frame inpoint cloud data and each image frame in image data respectively.

The executing body may identify the obstacles included in each pointcloud frame in the point cloud data, and may also identify the obstaclesincluded in each image frame in the image data, using a pre-trainedobstacle recognition model or an obstacle recognition algorithm (e.g., apoint cloud segmentation algorithm, or a feature extraction algorithm).Specifically, the executing body may input each point cloud frame in thepoint cloud data or each image frame in the image data into the obstaclerecognition model from an input side, such that recognized obstacles maybe obtained from an output side of the obstacle recognition model.

Step 2032: determining a similarity between an obstacle in each pointcloud frame and an obstacle in each image frame.

After recognizing the obstacles included in each point cloud frame andeach image frame, the executing body may compute the similarity betweenthe obstacle in each point cloud frame and the obstacle in each imageframe. Specifically, the executing body may extract features of theobstacles and compute the similarity between the obstacles based on adistance between feature vectors.

Step 2033: determining, in response to the similarity being greater thana preset similarity threshold, whether a time interval between the pointcloud frame and the image frame corresponding to two similar obstaclesis less than the time threshold.

For each obtained similarity, the executing body may determine whetherthe similarity is greater than the preset similarity threshold. Here,the similarity threshold may be used for characterizing a degree ofsimilarity between obstacles. If the similarity is greater than thesimilarity threshold, then the two obstacles are very similar. Then, theexecuting body may further determine the point cloud frame and the imageframe of the two similar obstacles. Then, the time interval between thepoint cloud frame and the image frame is computed, and whether the abovetime interval is greater than the time threshold is determined.

Step 2034: processing recognized obstacles based on a determiningresult, to determine the number of obstacles.

After obtaining the determining result, the executing body may processthe recognized obstacles based on the determining result, to determinethe number of obstacles. Specifically, in response to the time intervalbeing less than the time threshold, the executing body may identify thetwo obstacles as the same obstacle. In response to the time intervalbeing greater than or equal to the time threshold, the executing bodymay not identify the two obstacles as the same obstacle. It can beunderstood that if the two obstacles are identified as the sameobstacle, then the two obstacles are statisticized as one obstacle, whenstatisticizing the number of obstacles. If the two obstacles are notidentified as the same obstacle, then the two obstacles arestatisticized as two obstacles, when statisticizing the number ofobstacles. Thus, the number of recognized obstacles can be determined ateach time threshold.

In some alternative implementations of an embodiment, the executing bodymay determine the number of obstacles in the following ways that are notshown in FIG. 2: associating, in response to the time interval beingless than the time threshold, the two similar obstacles as the sameobstacle; and non-associating, in response to the time interval beinggreater than or equal to the time threshold, the two similar obstaclesas the same obstacle.

In some alternative implementations of an embodiment, the above methodmay further include the following steps that are not shown in FIG. 2:fusing, in response to the associating the two similar obstacles as thesame obstacle, the two similar obstacles based on the point cloud frameand the image frame corresponding to the two similar obstacles.

In response to the associating the two similar obstacles as the sameobstacle, the executing body may fuse the two similar obstacles based onthe point cloud frame and the image frame corresponding to the twosimilar obstacles. Thus, more accurate obstacle information can beobtained for guiding the driving of the autonomous vehicle.

Step 204: determining, based on obtained a plurality of numbers, andoutputting a target time threshold.

In the present embodiment, with the increase of the time threshold, themore are the similar obstacles likely to be associated, and the smalleris the number of obtained obstacles. Accordingly, the smaller is thetime threshold, the less are the similar obstacles likely to beassociated, and the larger is the number of obtained obstacles. Then,the executing body may determine a change rate of the numbers ofobstacles based on the obtained numbers, and determine the target timethreshold based on the change rate.

With further reference to FIG. 3, FIG. 3 is a schematic diagram of anapplication scenario of the method for outputting information accordingto the present embodiment. In the application scenario of FIG. 3, anautonomous vehicle 301 collects point cloud data through a lidar sensorprovided thereon, collects image data through a camera provided thereonduring a driving process, and sends the point cloud data and the imagedata to a server 302. The server 302 performs processing in steps201-204 for the point cloud data and the image data, to determine atarget time threshold, and sends the target time threshold to theautonomous vehicle 301. The autonomous vehicle 301 may fuse obstaclesduring the driving process based on the target time threshold.

The method for outputting information provided by embodiments of thepresent disclosure may first acquire obstacle data collected by avehicle during a driving process, where the obstacle data may includepoint cloud data and image data; then may determine a plurality of timethresholds based on a preset time threshold value range; may executeprocessing for each time threshold: identifying obstacles included ineach point cloud frame in the point cloud data and each image frame inthe image data respectively; then determining a similarity between anobstacle in each point cloud frame and an obstacle in each image frame;determining, in response to the similarity being greater than a presetsimilarity threshold, whether a time interval between the point cloudframe and the image frame of two similar obstacles is less than the timethreshold; and processing recognized obstacles based on a determiningresult, to determine the number of obstacles; and finally determines,based on obtained a plurality of numbers, and outputs a target timethreshold. The method of the present embodiment may determine the timethreshold during the obstacle data processing based on the numbers ofobstacles, thereby achieving automatic adjustment of the time thresholdwithout the need of manual adjustment.

With further reference to FIG. 4, a process 400 of determining a targettime threshold in the method for outputting information according to anembodiment of the present disclosure is shown. As shown in FIG. 4, themethod for outputting information according to the present embodimentmay determine a target time threshold through the following steps.

Step 401: determining a number-time threshold curve based on a pluralityof numbers and a time threshold corresponding to each number.

In the present embodiment, the executing body may obtain a number-timethreshold curve with the time threshold as the X axis, with the numberof obstacles as the Y axis, based on the obtained plurality of numbersand the time threshold corresponding to each number.

Step 402: determining a slope of the curve at each time threshold.

Then, the executing body may determine the slope of the curve at eachtime threshold based on an equation of the curve. The slope here canrepresent a change rate of the numbers of obstacles. The executing bodymay determine the target time threshold based on the obtained eachslope. For example, the executing body may use a time threshold with amaximum absolute value of the slope as the target time threshold.Alternatively, the executing body may compute an average value of theslopes, and then use a time threshold corresponding to the average valueas the target time threshold. It can be understood that the target timethreshold is determined based on the curve, and may be the same as, ordifferent from, the preset time threshold.

Step 403: determining a maximum value of absolute values of the slopes.

In the present embodiment, the executing body may determine the maximumvalue of the absolute values of the slopes. It can be understood thatthe less is the time threshold, i.e., the larger is the number ofobstacles, the more is the false detection likely to take place, and thehigher is the false detection rate. The greater is the time threshold,i.e., the smaller is the number of obstacles, the more is the misseddetection likely to take place, and the higher is the missed detectionrate. Here, a point corresponding to the maximum value is a point atwhich the number of obstacles decreases fastest, and is also anintersection point of a false detection rate curve and a misseddetection rate curve.

Step 404: using a time threshold corresponding to the maximum value as atarget time threshold.

The executing body may use the time threshold corresponding to themaximum value as the target time threshold.

The method for outputting information provided by embodiments of thepresent disclosure may automatically determine an appropriate timethreshold, thereby reducing the workload in the process of obstacle dataprocessing.

With further reference to FIG. 5, as an implementation of the methodshown in the above figures, an embodiment of the present disclosureprovides an apparatus for outputting information. An embodiment of theapparatus may correspond to an embodiment of the method shown in FIG. 2.The apparatus may be specifically applied to various electronic devices.

As shown in FIG. 5, the apparatus 500 for outputting information of thepresent embodiment includes: a data acquiring unit 501, a thresholddetermining unit 502, a data processing unit 503, and a targetdetermining unit 504.

The data acquiring unit 501 is configured to acquire obstacle datacollected by a vehicle during a driving process. The obstacle datainclude point cloud data and image data.

The threshold determining unit 502 is configured to determine aplurality of time thresholds based on a preset time threshold valuerange.

The data processing unit 503 is configured to execute the followingprocessing for each time threshold: identifying obstacles included ineach point cloud frame in the point cloud data and each image frame inthe image data respectively; determining a similarity between anobstacle in each point cloud frame and an obstacle in each image frame;determining, in response to the similarity being greater than a presetsimilarity threshold, whether a time interval between the point cloudframe and the image frame corresponding to two similar obstacles is lessthan the time threshold; and processing recognized obstacles based on adetermining result, to determine the number of obstacles.

The target determining unit is configured to determine, based onobtained a plurality of numbers, and output a target time threshold.

In some alternative implementations of the present embodiment, thethreshold determining unit 502 may be further configured to: select aplurality of points in the time threshold value range at a preset timeinterval, as the plurality of time thresholds.

In some alternative implementations of the present embodiment, the dataprocessing unit 503 may be further configured to: associate, in responseto the time interval being less than the time threshold, the two similarobstacles as the same obstacle; and non-associate, in response to thetime interval being greater than or equal to the time threshold, the twosimilar obstacles as the same obstacle.

In some alternative implementations of the present embodiment, the aboveapparatus 500 may further include a data fusing unit that is not shownin FIG. 5, and is configured to fuse, in response to the associating thetwo similar obstacles as the same obstacle, the two similar obstaclesbased on the point cloud frame and the image frame corresponding to thetwo similar obstacles.

In some alternative implementations of the present embodiment, thetarget determining unit 504 may be further configured to: determine anumber-time threshold curve based on the plurality of numbers and a timethreshold corresponding to each number; and determine a slope of thecurve at each time threshold, and determine the target time thresholdbased on each slope.

In some alternative implementations of the present embodiment, thetarget determining unit 504 may be further configured to: determine amaximum value of absolute values of the slopes; and use a time thresholdcorresponding to the maximum value as the target time threshold.

It should be understood that the unit 501 to unit 504 recorded in theapparatus 500 for outputting information correspond to the steps in themethod described in FIG. 2 respectively. Therefore, the operations andfeatures described above for the method for outputting information alsoapply to the apparatus 500 and the units included therein. Thedescription will not be repeated here.

Referring to FIG. 6 below, a schematic structural diagram adapted toimplement an electronic device 600 of some embodiments of the presentdisclosure is shown. The electronic device shown in FIG. 6 is merely anexample, and should not limit the functions and scope of use of someembodiments of the present disclosure.

As shown in FIG. 6, the electronic device 600 may include a processingapparatus (e.g., a central processing unit, or a graphics processor)601, which may execute various appropriate actions and processes inaccordance with a program stored in a read-only memory (ROM) 602 or aprogram loaded into a random access memory (RAM) 603 from a storageapparatus 608. The RAM 603 further stores various programs and datarequired by operations of the electronic device 600. The processingapparatus 601, the ROM 602, and the RAM 603 are connected to each otherthrough a bus 604. An input/output (I/O) interface 605 is also connectedto the bus 604.

In general, the following apparatuses may be connected to the I/Ointerface 605: an input apparatus 606 including a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, agyroscope, or the like; an output apparatus 607 including a liquidcrystal display device (LCD), a speaker, a vibrator, or the like; astorage apparatus 608 including a magnetic tape, a hard disk, or thelike; and a communication apparatus 609. The communication apparatus 609may allow the electronic device 600 to exchange data with other devicesthrough wireless or wired communication. While FIG. 6 shows theelectronic device 600 having various apparatuses, it should beunderstood that it is not necessary to implement or provide all of theapparatuses shown in the figure. More or fewer apparatuses may bealternatively implemented or provided. Each block shown in FIG. 6 mayrepresent an apparatus, or represent a plurality of apparatuses asrequired.

In particular, according to some embodiments of the present disclosure,the process described above with reference to the flow chart may beimplemented in a computer software program. For example, someembodiments of the present disclosure include a computer programproduct, which includes a computer program that is tangibly embedded ina computer readable medium. The computer program includes program codesfor executing the method as shown in the flow chart. In such anembodiment, the computer program may be downloaded and installed from anetwork via the communication apparatus 609, or be installed from thestorage apparatus 608, or be installed from the ROM 602. The computerprogram, when executed by the processing apparatus 601, implements theabove functions as defined by the method of some embodiments of thepresent disclosure. It should be noted that the computer readable mediumaccording to some embodiments of the present disclosure may be acomputer readable signal medium or a computer readable medium or anycombination of the above two. An example of the computer readable mediummay include, but is not limited to: electric, magnetic, optical,electromagnetic, infrared, or semiconductor systems, apparatuses,elements, or a combination of any of the above. A more specific exampleof the computer readable medium may include, but is not limited to:electrical connection with one or more pieces of wire, a portablecomputer disk, a hard disk, a random access memory (RAM), a read onlymemory (ROM), an erasable programmable read only memory (EPROM or flashmemory), an optical fiber, a portable compact disk read only memory(CD-ROM), an optical memory, a magnetic memory, or any suitablecombination of the above. In some embodiments of the present disclosure,the computer readable medium may be any tangible medium containing orstoring programs, which may be used by, or used in combination with, acommand execution system, apparatus or element. In some embodiments ofthe present disclosure, the computer readable signal medium may includea data signal in the base band or propagating as a part of a carrierwave, in which computer readable program codes are carried. Thepropagating data signal may take various forms, including but notlimited to an electromagnetic signal, an optical signal, or any suitablecombination of the above. The computer readable signal medium may alsobe any computer readable medium except for the computer readable medium.The computer readable medium is capable of transmitting, propagating ortransferring programs for use by, or used in combination with, a commandexecution system, apparatus or element. The program codes contained onthe computer readable medium may be transmitted with any suitablemedium, including but not limited to: wireless, wired, optical cable, RFmedium, etc., or any suitable combination of the above.

The computer readable medium may be included in the above electronicdevice; or a stand-alone computer readable medium without beingassembled into the electronic device. The computer readable mediumstores one or more programs. The one or more programs, when executed bythe electronic device, cause the electronic device to: acquire obstacledata collected by a vehicle during a driving process, the obstacle dataincluding point cloud data and image data; determine a plurality of timethresholds based on a preset time threshold value range; executefollowing processing for each time threshold: identifying obstaclesincluded in each point cloud frame in the point cloud data and eachimage frame in the image data respectively; determining a similaritybetween an obstacle in each point cloud frame and an obstacle in eachimage frame; determining, in response to the similarity being greaterthan a preset similarity threshold, whether a time interval between thepoint cloud frame and the image frame of two similar obstacles is lessthan the time threshold; and processing recognized obstacles based on adetermining result, to determine the number of obstacles; and determine,based on obtained a plurality of numbers, and output a target timethreshold.

A computer program code for executing operations in some embodiments ofthe present disclosure may be compiled using one or more programminglanguages or combinations thereof. The programming languages includeobject-oriented programming languages, such as Java, Smalltalk or C++,and also include conventional procedural programming languages, such as“C” language or similar programming languages. The program code may becompletely executed on a user's computer, partially executed on a user'scomputer, executed as a separate software package, partially executed ona user's computer and partially executed on a remote computer, orcompletely executed on a remote computer or server. In a circumstanceinvolving a remote computer, the remote computer may be connected to auser's computer through any network, including local area network (LAN)or wide area network (WAN), or be connected to an external computer (forexample, connected through the Internet using an Internet serviceprovider).

The flow charts and block diagrams in the accompanying drawingsillustrate architectures, functions and operations that may beimplemented according to the systems, methods and computer programproducts of the various embodiments of the present disclosure. In thisregard, each of the blocks in the flowcharts or block diagrams mayrepresent a module, a program segment, or a code portion, said module,program segment, or code portion including one or more executableinstructions for implementing specified logical functions. It should befurther noted that, in some alternative implementations, the functionsdenoted by the blocks may also occur in a sequence different from thesequences shown in the figures. For example, any two blocks presented insuccession may be executed substantially in parallel, or they maysometimes be executed in a reverse sequence, depending on the functionsinvolved. It should be further noted that each block in the blockdiagrams and/or flow charts as well as a combination of blocks in theblock diagrams and/or flow charts may be implemented using a dedicatedhardware-based system executing specified functions or operations, or bya combination of dedicated hardware and computer instructions.

The units involved in some embodiments of the present disclosure may beimplemented by software, or may be implemented by hardware. Thedescribed units may also be provided in a processor, for example,described as: a processor including a data acquiring unit, a thresholddetermining unit, a data processing unit, and a target determining unit.The names of the units do not constitute a limitation to such unitsthemselves in some cases. For example, the data acquiring unit may befurther described as “a unit configured to acquire obstacle datacollected by a vehicle during a driving process.”

The above description only provides an explanation of embodiments of thepresent disclosure and the technical principles used. It should beappreciated by those skilled in the art that the inventive scope of thepresent disclosure is not limited to the technical solutions formed bythe particular combinations of the above-described technical features.The inventive scope should also cover other technical solutions formedby any combinations of the above-described technical features orequivalent features thereof without departing from the concept of thepresent disclosure. Technical schemes formed by the above-describedfeatures being interchanged with, but not limited to, technical featureswith similar functions disclosed in the present disclosure are examples.

What is claimed is:
 1. A method for outputting information, comprising:acquiring obstacle data collected by a vehicle during a driving process,the obstacle data including point cloud data and image data; determininga plurality of time thresholds based on a preset time threshold valuerange; executing following processing for each time threshold:identifying obstacles included in each point cloud frame in the pointcloud data and each image frame in the image data respectively;determining a similarity between an obstacle in each point cloud frameand an obstacle in each image frame; determining, in response to thesimilarity being greater than a preset similarity threshold, whether atime interval between the point cloud frame and the image framecorresponding to two similar obstacles is less than the time threshold;and processing recognized obstacles based on a determining result, todetermine a number of obstacles; and determining, based on obtained aplurality of numbers, and outputting a target time threshold.
 2. Themethod according to claim 1, wherein the determining a plurality of timethresholds based on a preset time threshold value range comprises:selecting a plurality of points in the time threshold value range at apreset time interval, as the plurality of time thresholds.
 3. The methodaccording to claim 1, wherein the processing recognized obstacles basedon a determining result, to determine a number of obstacles comprises:associating, in response to the time interval being less than the timethreshold, the two similar obstacles as the same obstacle; andnon-associating, in response to the time interval being greater than orequal to the time threshold, the two similar obstacles as the sameobstacle.
 4. The method according to claim 3, wherein the method furthercomprises: fusing, in response to the associating the two similarobstacles as the same obstacle, the two similar obstacles based on thepoint cloud frame and the image frame corresponding to the two similarobstacles.
 5. The method according to claim 1, wherein the determining,based on obtained a plurality of numbers, and outputting a target timethreshold comprises: determining a number-time threshold curve based onthe plurality of numbers and a time threshold corresponding to eachnumber; and determining a slope of the curve at each time threshold, anddetermining the target time threshold based on each slope.
 6. The methodaccording to claim 5, wherein the determining the target time thresholdbased on each slope comprises: determining a maximum value of absolutevalues of the slopes; and using a time threshold corresponding to themaximum value as the target time threshold.
 7. An apparatus foroutputting information, comprising: at least one processor; and a memorystoring instructions, the instructions when executed by the at least oneprocessor, causing the at least one processor to perform operations, theoperations comprising: acquiring obstacle data collected by a vehicleduring a driving process, the obstacle data including point cloud dataand image data; determining a plurality of time thresholds based on apreset time threshold value range; executing following processing foreach time threshold: identifying obstacles included in each point cloudframe in the point cloud data and each image frame in the image datarespectively; determining a similarity between an obstacle in each pointcloud frame and an obstacle in each image frame; determining, inresponse to the similarity being greater than a preset similaritythreshold, whether a time interval between the point cloud frame and theimage frame of two similar obstacles is less than the time threshold;and processing recognized obstacles based on a determining result, todetermine a number of obstacles; and determining, based on obtained aplurality of numbers, and output a target time threshold.
 8. Theapparatus according to claim 7, wherein the determining a plurality oftime thresholds based on a preset time threshold value range comprises:selecting a plurality of points in the time threshold value range at apreset time interval, as the plurality of time thresholds.
 9. Theapparatus according to claim 7, wherein the processing recognizedobstacles based on a determining result, to determine a number ofobstacles comprises: associating, in response to the time interval beingless than the time threshold, the two similar obstacles as the sameobstacle; and non-associating, in response to the time interval beinggreater than or equal to the time threshold, the two similar obstaclesas the same obstacle.
 10. The apparatus according to claim 9, whereinthe operations further comprise: fusing, in response to the associatingthe two similar obstacles as the same obstacle, the two similarobstacles based on the point cloud frame and the image framecorresponding to the two similar obstacles.
 11. The apparatus accordingto claim 7, wherein the determining, based on obtained a plurality ofnumbers, and outputting a target time threshold comprises: determining anumber-time threshold curve based on the plurality of numbers and a timethreshold corresponding to each number; and determining a slope of thecurve at each time threshold, and determining the target time thresholdbased on each slope.
 12. The apparatus according to claim 11, whereinthe determining the target time threshold based on each slope comprises:determining a maximum value of absolute values of the slopes; and usinga time threshold corresponding to the maximum value as the target timethreshold.
 13. A non-transitory computer readable medium, storing acomputer program thereon, wherein the computer program, when executed bya processor, causes the processor to perform operations, the operationscomprising: acquiring obstacle data collected by a vehicle during adriving process, the obstacle data including point cloud data and imagedata; determining a plurality of time thresholds based on a preset timethreshold value range; executing following processing for each timethreshold: identifying obstacles included in each point cloud frame inthe point cloud data and each image frame in the image datarespectively; determining a similarity between an obstacle in each pointcloud frame and an obstacle in each image frame; determining, inresponse to the similarity being greater than a preset similaritythreshold, whether a time interval between the point cloud frame and theimage frame corresponding to two similar obstacles is less than the timethreshold; and processing recognized obstacles based on a determiningresult, to determine a number of obstacles; and determining, based onobtained a plurality of numbers, and outputting a target time threshold.14. The non-transitory computer readable medium according to claim 13,wherein the determining a plurality of time thresholds based on a presettime threshold value range comprises: selecting a plurality of points inthe time threshold value range at a preset time interval, as theplurality of time thresholds.
 15. The non-transitory computer readablemedium according to claim 13, wherein the processing recognizedobstacles based on a determining result, to determine a number ofobstacles comprises: associating, in response to the time interval beingless than the time threshold, the two similar obstacles as the sameobstacle; and non-associating, in response to the time interval beinggreater than or equal to the time threshold, the two similar obstaclesas the same obstacle.
 16. The non-transitory computer readable mediumaccording to claim 15, wherein the operations further comprise: fusing,in response to the associating the two similar obstacles as the sameobstacle, the two similar obstacles based on the point cloud frame andthe image frame corresponding to the two similar obstacles.
 17. Thenon-transitory computer readable medium according to claim 13, whereinthe determining, based on obtained a plurality of numbers, andoutputting a target time threshold comprises: determining a number-timethreshold curve based on the plurality of numbers and a time thresholdcorresponding to each number; and determining a slope of the curve ateach time threshold, and determining the target time threshold based oneach slope.
 18. The non-transitory computer readable medium according toclaim 17, wherein the determining the target time threshold based oneach slope comprises: determining a maximum value of absolute values ofthe slopes; and using a time threshold corresponding to the maximumvalue as the target time threshold.